def write_scenes(fname):
dic = read_json(fname)
folder = dic['folder']
fname = dic['filename']
print "Initializing a scene from " + folder
BB_ts_list, width, height = process_data.get_BB_ts_list(folder,
label="Biker")
params = read_json("params.json")
print params['nfold']
kf = KFold(n_splits=params['nfold'])
def mkdir(fname):
try:
os.mkdir(fname)
except:
pass
mkdir(join("scenes", fname))
mkdir(join("scenes", fname, "scenes"))
mkdir(join("scenes", fname, "sets"))
mkdir(join("scenes", fname, "train_sets"))
print[x.shape for x in BB_ts_list]
for j, (train_set, test_set) in enumerate(kf.split(BB_ts_list)):
train_set = [
x[1]
for x in filter(lambda x: x[0] in train_set, enumerate(BB_ts_list))
]
test_set = [
x[1]
for x in filter(lambda x: x[0] in test_set, enumerate(BB_ts_list))
]
test_scene = Scene(train_set, width, height)
#print "Display clusters"
#for k in range( test_scene.num_nl_classes ):
# from visualization_routines import visualize_cluster
# visualize_cluster( test_scene, k )
print "P(k) = "
print test_scene.P_of_c
print "\sum_k P(k) = {}".format(test_scene.P_of_c.sum())
print "Pickling scene and test set."
with open(join("scenes", fname, "scenes", "{}.pkl").format(j),
"w") as f:
pickle.dump(test_scene, f)
print "Pickled scene"
with open(join("scenes", fname, "sets", "{}.pkl").format(j), "w") as f:
pickle.dump(test_set, f)
print "Pickled the test set with {} agents".format(len(test_set))
with open(
join("scenes", fname, "train_sets", "{}.pkl").format(j),
"w") as f:
pickle.dump(train_set, f)
print "Pickled the test set with {} agents".format(len(train_set))
times_ret.append(times)
#ax = plt.gca()
#ax.set_ylim([-.1, 1.1])
#ax.set_ylabel("AUC")
#ax.set_xlabel("Frames")
#ax.plot(times, aucs)
#mkdir("images/{}".format(parad))
#mkdir("images/{}/{}".format(parad, name))
#mkdir("images/{}/{}/{}".format(parad, name, split))
#plt.savefig("images/AUC_{}_{}_split_{}_{}.png".format(parad, name, split, agent))
#plt.show()
return aucs_ret, times_ret
from json_help import read_json
order = read_json("scene_order.json")['order']
indices = read_json(argv[1])['indices']
for ind in indices:
fig = plt.figure()
ax = fig.gca()
ax.set_ylim([-.1, 1.1])
ax.set_ylabel("AUC")
ax.set_xlabel("Frames")
if len(ind) < 4:
inds = range(3)
else:
inds = ind[3]
if 2 in inds:
aucs, times = fn("kitani", ind[0], ind[1], [ind[2]])
ax.plot(times[0],
scene.P_of_c = np.ones_like(scene.P_of_c) / float(len(
scene.P_of_c))
scenes.append(scene)
elif file.endswith("set.pkl"):
with open(join("scenes", file), 'r') as f:
sets.append(pickle.load(f))
scene = scenes[0]
set = sets[0] #TODO: Probably should not use a python keyword here
random_sigmas = [
learn_sigma_RW(curves) for curves in map(lambda x: map(bbts, x), sets)
]
from json_help import read_json
_dic = read_json(join("config", "scene_order.json"))
params = read_json("config.json")
all_data = []
_order = _dic['order']
for folder in _order:
row = []
for i in range(params['nfold']):
num = "{}.pkl".format(i)
with open(join("scenes", "{}", "scenes", "{}").format(folder, num),'r') as f, \
open(join("scenes", "{}", "sets", "{}").format(folder, num),'r') as g, \
open(join("scenes", "{}", "train_sets", "{}").format(folder, num),'r') as h:
row.append((pickle.load(f), pickle.load(g), pickle.load(h)))
assert len(row) == params['nfold']
all_data.append(row)
aucs.append(auc(fpr, tpr))
plt.title("AUC vs time")
ax = plt.gca()
ax.set_ylim([-.1, 1.1])
ax.set_ylabel("AUC")
ax.set_xlabel("Frames")
ax.plot(times, aucs)
#plt.savefig("images/kitani/AUC_vs_t_for_{}.png".format(name))
#plt.show()
return aucs, times
from json_help import read_json
order = read_json("scene_order.json")['order']
params = read_json("params.json")
rng = ['death']
sm = lambda x, y: x + y
indexes = [0]
paths_kit = reduce(sm,
[["pickles/kitani/{}/{}/".format(x, j) for j in indexes]
for x in rng])
paths_ours = reduce(sm, [["pickles/ours/{}/{}/".format(x, j) for j in indexes]
for x in rng])
paths_lin = reduce(sm,
[["pickles/linear/{}/{}/".format(x, j) for j in indexes]
for x in rng])
paths_rand = reduce(sm, [["pickles/rand/{}/{}/".format(x, j) for j in indexes]
for x in rng])
import json
import os
from shutil import copy as cp
def mkdir(fname):
try:
os.mkdir(fname)
except:
pass
from json_help import read_json
dic = read_json("scene_order.json")
order = dic['order']
params = read_json("params.json")
for name in order:
mkdir("kitani/{}".format(name))
for ind in range(params['nfold']):
mkdir("kitani/{}/{}".format(name, ind))
mkdir("kitani/{}/{}/output".format(name, ind))
mkdir("kitani/{}/{}/frames".format(name, ind))
cp("kitani/{}_feat.xml".format(name),
"kitani/{}/{}/walk_feature_maps.xml".format(name, ind))
cp("kitani/{}_topdown.jpg".format(name),
"kitani/{}/{}/walk_birdseye.jpg".format(name, ind))
cp("kitani/{}_reward.txt".format(name),
"kitani/{}/{}/walk_reward_weights.txt".format(name, ind))